Simulation of olive fruit yield in Tuscany through the integration of remote sensing and ground data.

The current paper presents the development and testing of a multi-step methodology which integrates remotely sensed and ancillary data to estimate olive (Olea europaea L.) fruit yield in Tuscany (Central Italy). The processing of very high resolution (Ikonos) and high resolution (Landsat ETM+) images provides a map of olive tree canopy cover fraction for all Tuscany olive yards, which is used to extract olive tree NDVI values from MODIS imagery. The combination of these values with standard meteorological data within a modified parametric model (C-Fix) enables the prediction of daily olive tree gross primary production (GPP) for ten years (2000-2009). These GPP estimates are then joint to the respiration estimates of a bio-geochemical model (BIOME-BGC) to simulate olive tree net primary production (NPP). The NPP accumulated over proper periods of the ten growing seasons is finally converted into olive fruit yield, whose accuracy is assessed through comparison with provincial statistics. The methodology is only partly capable of capturing spatial and temporal olive fruit yield variability at province level, but can accurately reproduce inter-year yield variation over the entire region. The paper concludes with a discussion of the results achieved and with considerations on the research prospects.